import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size,out_size]))
    biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs,Weights) + biases

    if activation_function is None :
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)

    return outputs


x_data = np.linspace(-1,1,300)[:,np.newaxis]        # 线段，输入值
noise = np.random.normal(0,0.05,x_data.shape)       # 噪点
y_data = np.square(x_data) - 0.5 + noise            # 加上噪点的输出值

xs = tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])

l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)          # 隐藏层，一个神经元，隐藏层，10个神经元
prediction = add_layer(l1,10,1,activation_function=None)             # 输出层，1个神经元

# 输出层与实际输出值的偏差
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction), reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)  # 以0.1的步长减小输出偏差

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
plt.ion()
plt.show()

for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i%50 == 0:
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
        try:
            ax.lines.remove(lines[0])
        except Exception:
            pass
        prediction_value = sess.run(prediction,feed_dict={xs:x_data})
        lines = ax.plot(x_data,prediction_value,'r-',lw=5)
        plt.pause(0.1)




